As previously discussed, modern image generators (and StyleGAN in particular), are known to have a well-behaved latent space. Such a latent space is conductive for tasks such as image editing and image-to-image translation [1]}, [2]}, [3]}, [4]}, [5]}. The ability to apply such manipulations to real images is of particular interest, leading to an ever-increasing list of methods for GAN inversion [6]}, [7]}, [4]}, [9]}, [10]}. We show that our generators can still support such manipulation tasks, using the same techniques and inversion methods. Indeed, as we outline below, our model can even reuse off-the-shelf models pre-trained on the source generator's domain, with no need for additional fine-tuning.
